Task Scheduling for Public Clouds Using a Fuzzy Controller-Based Priority- and Deadline-Aware Approach

With the increasing predominance of public cloud computing, managing the cost of usage for end users has become vital in importance. Task scheduling has emerged as an important target of research in this area. The present work considers the problem of assigning tasks with different priorities to clouds, with the main requirement being to ensure the meeting of deadlines for high-priority tasks at all costs. The idea is to use as many public cloud resources as needed to satisfy this constraint, even if it means incurring more cost. To solve this problem, the present work proposes a two-stage approach that uses a fuzzy logic controller and heuristic-based task rearrangement. The proposed approach is compared with the FCFS-EDF, SJF-EDF, and Random-EDF approaches using synthetic and GoCJ datasets, and it demonstrates the ability to meet all the primary constraints. The experiments also demonstrate that the required constraints do not necessarily require a higher cost expenditure. It is also shown that if a higher expenditure does occur for a particular task set, the proposed approach is able to keep the rise in cost minimal.